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Type 2 diabetes (T2D) is characterized by low-grade inflammation. Low-density neutrophils (LDNs) represent normally less than 2% of total neutrophils but increase in multiple pathologies, releasing inflammatory cytokines and neutrophil extracellular traps (NETs). We assessed the count and role of high-density neutrophils (HDNs), LDNs, and NET-related activities in patients with T2D. HDNs and LDNs were purified by fluorescence-activated cell sorting (FACS) and counted by flow cytometry. Circulating inflammatory and NETs biomarkers were measured by ELISA (Enzyme Linked Immunosorbent Assay). NET formation was quantified by confocal microscopy. Neutrophil adhesion onto a human extracellular matrix (hECM) was assessed by optical microscopy. We recruited 22 healthy volunteers (HVs) and 18 patients with T2D. LDN counts in patients with diabetes were significantly higher (160%), along with circulating NETs biomarkers (citrullinated H3 histone (H3Cit), myeloperoxidase (MPO), and MPO-DNA (137%, 175%, and 69%, respectively) versus HV. Circulating interleukins (IL-6 and IL-8) and C-Reactive Protein (CRP) were significantly increased by 117%, 171%, and 79%, respectively, in patients compared to HVs. Isolated LDNs from patients expressed more H3Cit, MPO, and NETs, formed more NETs, and adhered more on hECM compared to LDNs from HVs. Patients with T2D present higher levels of circulating LDN- and NET-related biomarkers and associated pro-inflammatory activities. [ABSTRACT FROM AUTHOR]

European agriculture faces numerous challenges, and agroecology has emerged as a promising alternative. To facilitate this transition, integrating agroecology into agricultural education is crucial. However, its systemic and interdisciplinary nature makes it difficult to teach and learn. To support teaching, the "SErious Game for AgroEcology" (SEGAE) was developed. SEGAE is a simulated mixed crop–livestock farm model in which players can learn by doing. This study aims to (1) assess the effectiveness of SEGAE in a 4-h lesson led by a single teacher, (2) examine the potential added value of classroom-based lessons compared to a fully online setting, and (3) analyze how students from different disciplinary backgrounds acquire agroecological knowledge. A teacher conducted three identical 4-h lessons using SEGAE, involving undergraduate bioengineering students from four specializations. One lesson was conducted fully online, while the other two were classroom-based. Each lesson included theory, game sessions, and pre- and post-surveys to assess knowledge acquisition. Student scores were converted to percentages and analyzed using descriptive statistics, paired t -tests, and multiple regressions. Feedback on the learning experience was collected through 36 statements related to 'flow' – the level of immersion in a task. A two-factor ANOVA and a Tukey test were performed. A PCA explored the flow-knowledge acquisition relationship. The results show that students significantly increased their knowledge in agroecology. No significant difference between the online and classroom-based lessons on knowledge acquisition could be demonstrated. However, a negative effect was found between the online session and the reported flow levels. A striking result was the increased performance of students upon answering open-ended questions, which required them to articulate knowledge. Moreover, most students enjoyed the game (91 %) and believed it enhanced their agroecology knowledge (92 %). SEGAE, in a 4-h lesson with a single teacher, is a relevant tool for teaching agroecology, achieving comparable results in knowledge acquisition and flow to a longer workshop. The impact is particularly evident in open-ended questions. The study provides complementary evidence of a link between the increase in absolute knowledge and two factors of flow: feedback and social interaction. [Display omitted] • Playing SEGAE in a 4-h lesson with a single teacher is an effective tool for learning agroecology. • The majority of students enjoyed SEGAE, recommended it to their peers, and experienced an increase in their knowledge. • A high level of immersion ('flow') was observed. • These outcomes are comparable to those of a 5-day workshop. • A significant improvement in open-ended questions was noted in this present study. [ABSTRACT FROM AUTHOR]

Background: Neutrophils induce the synthesis and release of angiopoietin 1 (Ang1), a cytosolic growth factor involved in angiogenesis and capable of inducing several pro-inflammatory activities in neutrophils. Neutrophils also synthesize and release neutrophil extracellular traps (NETs), comprised from decondensed nuclear DNA filaments carrying proteins such as neutrophil elastase (NE), myeloperoxidase (MPO), proteinase 3 (PR3) and calprotectin (S100A8/S100A9), which together, contribute to the innate immune response against pathogens (e.g., bacteria). NETs are involved in various pathological conditions through pro-inflammatory, pro-thrombotic and endothelial dysfunction effects and have recently been found in heart failure (HF) and type 2 diabetes (T2DM) patients. The aim of the present study was to investigate the role of NETs on the synthesis and release of Ang1 by the neutrophils in patients with T2DM and HF with preserved ejection fraction (HFpEF) (stable or acute decompensated; ADHFpEF) with or without T2DM. Results: Our data show that at basal level (PBS) and upon treatment with LPS, levels of NETs are slightly increased in patients suffering from T2DM, HFpEF ± T2DM and ADHF without (w/o) T2DM, whereas this increase was significant in ADHFpEF + T2DM patients compared to healthy control (HC) volunteers and ADHFpEF w/o T2DM. We also observed that treatments with PMA or A23187 increase the synthesis of Ang1 (from 150 to 250%) in HC and this effect is amplified in T2DM and in all cohorts of HF patients. Ang1 is completely released (100%) by neutrophils of all groups and does not bind to NETs as opposed to calprotectin. Conclusions: Our study suggests that severely ill patients with HFpEF and diabetes synthesize and release a greater abundance of NETs while Ang1 exocytosis is independent of NETs synthesis. [ABSTRACT FROM AUTHOR]

Background: Seed endophytes have a significant impact on plant health and fitness. They can be inherited and passed on to the next plant generation. However, the impact of breeding on their composition in seeds is less understood. Here, we studied the indigenous seed microbiome of a recently domesticated perennial grain crop (Intermediate wheatgrass, Thinopyrum intermedium L.) that promises great potential for harnessing microorganisms to enhance crop performance by a multiphasic approach, including amplicon and strain libraries, as well as molecular and physiological assays. Results: Intermediate wheatgrass seeds harvested from four field sites in Europe over three consecutive years were dominated by Proteobacteria (88%), followed by Firmicutes (10%). Pantoea was the most abundant genus and Pantoea agglomerans was identified as the only core taxon present in all samples. While bacterial diversity and species richness were similar across all accessions, the relative abundance varied especially in terms of low abundant and rare taxa. Seeds from four different breeding cycles (TLI C3, C5, C704, C801) showed significant differences in bacterial community composition and abundance. We found a decrease in the relative abundance of the functional genes nirK and nifH as well as a drop in bacterial diversity and richness. This was associated with a loss of amplicon sequence variants (ASVs) in Actinobacteria, Alphaproteobacteria, and Bacilli, which could be partially compensated in offspring seeds, which have been cultivated at a new site. Interestingly, only a subset assigned to potentially beneficial bacteria, e.g. Pantoea, Kosakonia, and Pseudomonas, was transmitted to the next plant generation or shared with offspring seeds. Conclusion: Overall, this study advances our understanding of the assembly and transmission of endophytic seed microorganisms in perennial intermediate wheatgrass and highlights the importance of considering the plant microbiome in future breeding programs. [ABSTRACT FROM AUTHOR]

No-tillage (NT) is considered the least invasive conservation agriculture technique and has shown to be the effective in increasing soil C stocks, and reducing losses compared to others tillage systems. In Italy, the Veneto Region was the first to establish a subsidies scheme aimed at promoting the adoption of NT practices. This program encourages farmers to perform direct seeding, alternate autumn and winter crops and maintain soil cover throughout the year by leaving crop residues or sowing cover crops. The goals of this study were to: (i) compare the CO 2 emission and soil C sequestration patterns of agricultural soils under CT and NT management practices in the Veneto region and (ii) analyse the potential mid-term benefits (2010–2025) of NT management in terms of soil organic C dynamics and CO 2 balance. Agronomic data and soil organic carbon levels were measured from 2010 to 2014 in eight farms in the Veneto region that had adopted CT and NT techniques. Field measurements were used to calibrate first and then validate the SALUS model to compare the mid-term impact of CT and NT practices using climate projections. SOC carbon pools in the model were initialized using the procedure described in Basso et al. (2011c) . This is the first study to employ a model using such an extensive dataset at the farm level to assess the CT and NT strategies within this region. Results of this research will assist farmers and policy makers in the region to define the tillage systems most suited to improve soil C stocks and thereby minimize CO 2 emissions from agricultural soils. Overall, simulations indicated that SOC stocks can decrease under both CT and NT regimes, however SOC oxidation rates were substantially lower under NT. Critically, the greatest reduction in CO 2 emission was observed when NT was adopted in soil with high levels of SOM. This highlights the benefits of NT adoption in terms of soil fertility preservation and CO 2 emissions mitigation. [ABSTRACT FROM AUTHOR]

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005–2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha − 1 applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha − 1 applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching. [ABSTRACT FROM AUTHOR]

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha) of the distribution. [ABSTRACT FROM AUTHOR]

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. [ABSTRACT FROM AUTHOR]

LAI of winter wheat ( Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: extended Kalman filtering (EKF), particle filtering (PF), and variational filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation. [ABSTRACT FROM AUTHOR]

Abstract: An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination of the model parameters. In these cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. This work addresses the problem of monitoring and modeling a leaf area index and soil moisture model (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), particle filter (PF), and the more recently developed technique variational filter (VF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the leaf-area index LAI , the volumetric water content of the soil layer 1, HUR1 and the volumetric water content of the soil layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of number of estimated model parameters on the accuracy and convergence of these techniques are also assessed. The results of both comparative studies show that the PF provides a higher accuracy than the EKF, which is due to the limited ability of the EKF to handle highly nonlinear processes. The results also show that the VF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the VF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the VF can still provide both convergence as well as accuracy related advantages over other estimation methods. [Copyright &y& Elsevier]

• IWG reproductive growth requires a vernalization treatment • In spring, IWG reproductive growth is associated with GDD accumulation and photoperiodic response • GDD-daylength coupled dynamics drive IWG sward functional changes during reproductive growth • GDD-daylength coupled dynamics can inform and improve agronomic management. Intermediate wheatgrass (Thinopyrum intermedium (Host) Barkworth & D.R. Dewey) is being developed for use as a new perennial grain crop through breeding and agronomic research. However, progress has been hampered by lack of understanding of environmental requirements for flowering and grain production. Therefore, we developed a phenology model for IWG adapted from the STICS soil-crop model. The model was compliant with experimental results (relative root mean square error = 0.03). The optimal vernalizing temperature was between 4 and 5°C, optimal daylength between 13 and 14h, while daylength below 11h slowed reproductive development. Vernalization requirement was found to be a constraining inductive process. Including a photoperiod limitation to the model with temperature improved its ability to predict induction at various latitudes. Therefore, timing and duration of vegetative vs. reproductive growth may differ between environments and change reproductive tiller elongation earliness, weed competitiveness, management timing, and stress conditions during phases critical to grain yield. Accurate phenology models will enable optimal field management and inform future breeding strategies. However, plasticity may lead to divergent ideotypes under various agroecosystems. [ABSTRACT FROM AUTHOR]

The reflectance of wheat crops provides information on their architecture or physiology. However, the methods currently used for close-range reflectance computation do not allow for the separation of the wheat canopy organs: the leaves and the ears. This study details a method to achieve high-throughput measurements of wheat reflectance at the organ scale. A nadir multispectral camera array and an incident light spectrometer were used to compute bi-directional reflectance factor (BRF) maps. Image thresholding and deep learning ear detection allowed for the segmentation of the ears and the leaves in the maps. The results showed that the BRF measured on reference targets was constant throughout the day but varied with the acquisition date. The wheat organ BRF was constant throughout the day in very cloudy conditions and with high sun altitudes but showed gradual variations in the morning under sunny or partially cloudy sky. As a consequence, measurements should be performed close to solar noon and the reference panel should be captured at the beginning and end of each field trip to correct the BRF. The method, with such precautions, was tested all throughout the wheat growing season on two varieties and various canopy architectures generated by a fertilization gradient. The method yielded consistent reflectance dynamics in all scenarios. [ABSTRACT FROM AUTHOR]

Improving agricultural nitrogen management is one of the key objectives of the recent Green Deal in Europe. Current technological developments in agriculture offer new opportunities to improve nitrogen fertilization practices. The aim of this study was to adapt to Sentinel-2 data a proven delineation method initially developed for yield maps, in order to facilitate precise nitrogen management by farmers. The study was conducted in two steps. Firstly, an analysis at annual level was conducted to assess the relationship between vegetation indices and yield at the subfield scale, for different sensing period. The second step consisted in performing a pluri- annual analysis through the delineation of management zones and compare the results achieved from yield maps and from NDVI maps. Among different vegetation indices, NDVI proved to be an interesting candidate for subfield detection of yield variation, specifically when the index was sensed was sensed around the second half of May. In this area, this period usually corresponds to phenological development between the flag leaf stage and heading stage, just prior the initiation of winter wheat flowering. Using NDVI maps within Blackmore's delineation approach instead of yield maps. Allowed to reach an accuracy of 69% on zone classification. However, as yields and NDVI distribution do not respond to similar statistical distributions, we considered that the delineation threshold used to differentiate high from low yielding zones had to be adapted. The adaptation of the "performance threshold" in favor of the median NDVI, made it possible to achieve a higher accuracy (71%) of the delineation. But above all, the improvement lies also in a more robust satellite-based delineation. [ABSTRACT FROM AUTHOR]

The challenge to sustain food security while halting the loss of biodiversity and soil quality might be achieved by a transformation in agriculture from high-input management of annual crops to a more nature-based solution introducing perennial cropping systems. This study analysed earthworm communities (numbers, biomass, ecological categories) and diversity over two years, from annual wheat and perennial intermediate wheatgrass (IWG, Thinopyrum intermedium , Kernza®) within the EU-Biodiversa project NAPERDIV from Southern to Northern Europe. Study sites in France, Belgium and Sweden represented diverse soil, climatic and plant growth conditions. In total, 16 species were identified with IWG in France having the highest (13) and annual wheat in Belgium and Sweden the lowest (7) species numbers. Improved biodiversity under perennial wheat was indicated by alpha-diversity indices (Simpson index, Shannon-Weaver index, Evenness). Earthworm abundance and biomass were generally significantly higher in IWG across the three sites (GLMM model). The overall mean earthworm number under IWG was 424.7 No. m−2 compared to 164.7 No. m−2 for annual wheat. Mean earthworm biomass under IWG was 83.7 g m−2 relative to 45.9 g m−2 under annual wheat, respectively. Remarkably, mean number of juvenile earthworms was several times higher on IWG sites relative to the annual comparatives. Moreover, endogeic and epigeic earthworms were supported on the IWG plots. Beta diversity (Sorensen coefficient) emphasised highest similarity between Belgium and Sweden and lowest between France and Sweden, indicating a possible South to North distribution within Western Europe. The Canonical Correspondence Analysis showed discrete clusters for study sites and species distribution (including the subtypes of Allolobophora chlorotica) in relation to soil parameters (pH, soil texture, TOC, TN, WHC, C–N ratio). The CCA additionally discriminated between annual and perennial plots in France. In summary, earthworm communities were more diverse under IWG and seemed to follow a South to North gradient. • Earthworm abundance and biomass was significantly increased under perennial grain. • Endogeic and epigeic earthworm species were promoted under perennial grain (IWG). • Alpha- and Beta-diversity indicated an increased earthworm diversity under IWG. • Earthworm species diversity reflected a South to North transect for Western Europe. • The Allolobophora chlorotica subtypes are distributed along soil characteristics. [ABSTRACT FROM AUTHOR]

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%. [ABSTRACT FROM AUTHOR]

Phosphorus deficiency induces biochemical and morphological changes which affect crop yield and production. Prompt fluorescence signal characterizes the PSII activity and electron transport from PSII to PSI, while the modulated light reflection at 820 (MR 820) nm investigates the redox state of photosystem I (PSI) and plastocyanin (PC). Therefore, combining information from modulated reflection at 820 nm with chlorophyll a fluorescence can potentially provide a more complete understanding of the photosynthetic process and integrating other plant physiological measurements may help to increase the accuracy of detecting the phosphorus deficiency in wheat leaves. In our study, we combined the chlorophyll a fluorescence and MR 820 signals to study the response of wheat plants to phosphorus deficiency as indirect tools for phosphorus plant status characterization. In addition, we studied the changes in chlorophyll content index, stomatal conductance (gs), root morphology, and biomass of wheat plants. The results showed an alteration in the electron transport chain as a specific response to P deficiency in the I-P phase during the reduction of the acceptor side of PSI. Furthermore, P deficiency increased parameters related to the energy fluxes per reaction centers, namely ETo/RC, REo/RC, ABS/RC, and DIo/RC. P deficiency increased the values of MRmin and MRmax and decreased νred, which implies that the reduction of PSI and PC became slower as the phosphorus decreased. The principal component analysis of the modulated reflection and chlorophyll a fluorescence parameters, with the integration of the growth parameters as supplementary variables, accounted for over 71% of the total variance in our phosphorus data using two components and provided a reliable information on PSII and PSI photochemistry under P deficiency. [ABSTRACT FROM AUTHOR]

The development of a perennial grain offers opportunities to diversify annual crop rotations, with potential benefits in terms of soil protection. Perennials could also reduce weed development over time through year-round soil cover and longer growing seasons. However, whether weeds would actually decrease remains mainly theoretical, with field data on perennial grains remaining sparse. Qualitative changes might also have an effect, because disturbance and modifications to resource regimes drive shifts in weed communities. Here, we analyzed weed abundance, composition, and traits in three arable fields containing perennial grain over a 4-year period. Specifically, intermediate wheatgrass (IWG) perennial grain (Kernza) was cultivated. IWG grain production was maximal during the first and second growing seasons (899 and 854 kg.ha-1 respectively), with total biomass production peaking in the second year (mean: 11.9 t DM.ha-1). However, reproductive effort noticeably dropped in the third and fourth years. Weed biomass also gradually reduced during the fall of all years (mean: ∼0.4–∼0.03 t DM.ha-1), but remained constant in spring (mean: 1–2 t DM.ha-1). During spring, the community composition of weeds shifted from broadleaves to grass species and, to a lesser extent, from annuals to perennials, with weed species having an earlier phenology than IWG. Thus, relative fitness appeared to be the strongest driver of weed community composition in aging IWG stands. Weed species richness systematically declined over the years. Specific leaf area, height, light, and nitrogen requirement index of weed communities provided poor descriptors of community shifts; however, all weed species with high light requirements were excluded from aging IWG stands. IWG capacity to compete with weeds might have been undermined by its late growing cycle, absence of forage harvest during the growing season, and substantial initial weed development during establishment (first year of growth). Thus, before IWG establishment, initial site conditions and land use history should be considered to implement the best management strategies for each field. Important weed development in perennial grain fields could lead to high weeding requirement, either mechanical or chemical, in conflict with the initial principle of increasing sustainability and reducing inputs. • Weeds were studied during four growing seasons on three on-farm trials. • Highest grain yields were obtained in 1rst and 2nd years, and in 2nd year for biomass. • Weed biomass remained constant in spring across years. • Weed community shift was mostly linked to grass species increase against broadleaves. • Other traits are useful to understand weed communities favored under aging stands. [ABSTRACT FROM AUTHOR]

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term. [ABSTRACT FROM AUTHOR]

Primary graft dysfunction (PGD) is characterized by alveolar epithelial and vascular endothelial damage and inflammation, lung edema and hypoxemia. Up to one-third of recipients develop the most severe form of PGD (Grade 3; PGD3). Animal studies suggest that neutrophils contribute to the inflammatory process through neutrophil extracellular traps (NETs) release (NETosis). NETs are composed of DNA filaments decorated with granular proteins contributing to vascular occlusion associated with PGD. The main objective was to correlate NETosis in PGD3 (n = 9) versus non-PGD3 (n = 27) recipients in an exploratory study. Clinical data and blood samples were collected from donors and recipients pre-, intra- and postoperatively (up to 72 h). Inflammatory inducers of NETs' release (IL-8, IL-6 and C-reactive protein [CRP]) and components (myeloperoxidase [MPO], MPO-DNA complexes and cell-free DNA [cfDNA]) were quantified by ELISA. When available, histology, immunohistochemistry and immunofluorescence techniques were performed on lung biopsies from donor grafts collected during the surgery to evaluate the presence of activated neutrophils and NETs. Lung biopsies from donor grafts collected during transplantation presented various degrees of vascular occlusion including neutrophils undergoing NETosis. Additionally, in recipients intra- and postoperatively, circulating inflammatory (IL-6, IL-8) and NETosis biomarkers (MPO-DNA, MPO, cfDNA) were up to 4-fold higher in PGD3 recipients compared to non-PGD3 (p = 0.041 to 0.001). In summary, perioperative elevation of NETosis biomarkers is associated with PGD3 following human lung transplantation and these biomarkers might serve to identify recipients at risk of PGD3 and initiate preventive therapies. [ABSTRACT FROM AUTHOR]

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures. [ABSTRACT FROM AUTHOR]

Process-based soil-crop models are widely used in agronomic research. They are major tools for evaluating climate change impact on crop production. Multi-model simulation studies show a wide diversity of results among models, implying that simulation results are very uncertain. A major path to improving simulation results is to propose improved calibration practices that are widely applicable. This study proposes an innovative generic calibration protocol. The two major innovations concern the treatment of multiple output variables and the choice of parameters to estimate, both of which are based on standard statistical procedure adapted to the particularities of soil-crop models. The protocol performed well in a challenging artificial-data test. The protocol is formulated so as to be applicable to a wide range of models and data sets. If widely adopted, it could substantially reduce model error and inter-model variability, and thus increase confidence in soil-crop model simulations. • A new protocol for crop-soil model calibration is proposed. • The choice of parameters to estimate is based on statistical model selection. • Multiple variables are fit simultaneously using weighted least squares. • The protocol was tested using artificial data with multiple observed variables. • The calibrated model worked well including for new environments. [ABSTRACT FROM AUTHOR]

In Hesbaye region (Belgium) with a loamy soil and under temperate climatic condition, winter wheat is a key component of agricultural rotations. As part of these rotations, soil management is a known driver of soil fertility and carbon storage. However, it could also influence the weed flora. In this study, the long-term effect of four soil management on the expressed and potential weed flora was examined. Soil management levers were i) the export (OUT) or restitution (IN) of crop residues and ii) the burial of residues by conventional tillage (mouldboard ploughing 25 cm depth, CT) or reduced tillage (cultivator ploughing 10 cm depth, RT). The weed seedbank and expressed flora in winter wheat were characterized. Weed diversity was assessed using the Richness and the Shannon diversity index. Then, the impact of flora and management on yield were investigated. Tillage management showed little impact on weed diversity with only a slight increase in diversity in reduced tillage. However, reduced tillage resulted in a higher weed seedling density and a higher weed density than conventional tillage, which indirectly led to yield losses. Exporting residues had no clear effect on weeds. In conclusion, within cropping systems based on the cultivation of wheat, reduced tillage can pose problems for the long-term management of the weed flora, and great attention has to be paid to its management. • Long-term residue exportation has no effect on weed diversity and density. • Long-term reduced tillage has a little effect on weed diversity. • Long-term reduced tillage promotes high weed density. • Tillage has indirect effect on yield by the attenuation of weed pressure.. [ABSTRACT FROM AUTHOR]

Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras' positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy. [ABSTRACT FROM AUTHOR]

During its growth, winter wheat (Triticum aestivum L.) can be impacted by multiple stresses involving fungal diseases that are responsible for high yield losses. Enhancing the breeding and the identification of resistant cultivars could be achieved by collecting automated and reliable information at the plant level. This study aims to estimate the severity of stripe rust (SR), brown rust (BR) and septoria tritici blotch (STB) in natural conditions and to highlight wavebands of interest, based on images acquired through a multispectral camera embedded on a ground-based platform. The severity of the three diseases has been assessed visually in an agronomic trial involving five wheat cultivars with or without fungicide treatment. An acquisition system using multispectral imagery covering the visible and near-infrared range has been set up at the canopy level. Based on spectral and textural features, estimations of area under disease progress curve (AUDPC) were performed by means of artificial neural networks (ANN) and partial least squares regression (PLSR). Supervised classification was also implemented by means of ANN. The ANN performed better at estimating disease severity with R2 of 0.72, 0.57 and 0.65 for STB, SR and BR respectively. Discrimination in two classes below or above 100 AUDPC reached an accuracy of 81% (κ = 0.60) for STB. This study, which combined the effect of date, cultivar and multiple disease infections, managed to highlight a few wavebands for each disease and took a step further in the development of a machine vision-based approach for the characterisation of fungal diseases in natural conditions. • Disease infection is mostly hidden by the reflectance changes due to growth stage. • Textural features are essential due to their lower dependency on growth stage. • The severity of septoria tritici blotch has been predicted with a R2 of 0.72. • Wavebands in the red, red edge and near infrared were the most selected for the three fungal diseases. [ABSTRACT FROM AUTHOR]

• Maize growth models differ widely in their simulations of soil temperature. • Root-mean-square errors ranged from about 1.5 °C to 5.1 °C. • APSIM, ecosys, JULES, Expert-N, SLFT, and maizsim were the best six models. • Most of the best models used a numeric energy balance on each soil layer. • More recent soil thermal conductivity routines might help improve the models. Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predictions of soil processes like seed germination, decomposition, nitrification, evaporation, and carbon sequestration. To assess how well such models can simulate soil temperature, herein we present results of an inter-comparison study of 33 maize (Zea mays L.) growth models. Among the 33 models, four of the modeling groups contributed results using differing algorithms or "flavors" to simulate evapotranspiration within the same overall model family. The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. The range of simulated soil temperatures was large (about 10–15 °C) from the coolest to warmest models across whole growing seasons from bare soil to full canopy and at both shallow and deeper depths. Within model families, there were no significant differences among their simulations of soil temperature due to their differing evapotranspiration method "flavors", so root-mean-square-errors (RMSE) were averaged within families, which reduced the number of soil temperature model families to 13. The model family RMSEs averaged over all 20 treatment-years and 2 depths ranged from about 1.5 to 5.1 °C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert-N, SLFT, and MaizSim. Five of these best models used a numerical iterative approach to simulate soil temperature, which entailed using an energy balance on each soil layer. whereby the change in heat storage during a time step equals the difference between the heat flow into and that out of the layer. Further improvements in the best models for simulating soil temperature might be possible with the incorporation of more recently improved routines for simulating soil thermal conductivity than the older routines now in use by the models. [Display omitted] [ABSTRACT FROM AUTHOR]

• Eddy covariance measurements were used to inter-compare 29 maize models in their ability to simulate evapotranspiration (ET). • There was a huge range among the models in their simulations of ET for the initial blind phase, which continued even as more information was supplied. • Medians of the 29 models were generally close to observations. • Both simple and complex models were among the best in their ability to simulate ET. • Widely used models were consistently among the best, presumably because large numbers of users with a wide range of conditions led to improvements. Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first "blind" phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET. [ABSTRACT FROM AUTHOR]

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. The projected impact of 1.5 and 2.0°C warming above the pre‐industrial period on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impacts of warming of <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. [ABSTRACT FROM AUTHOR]

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations. One way of estimating the projected impact of climate change on crops is to use crop models. There is a large variability in results of different crop models, but it has been observed that the mean or median of a multimodel ensemble (MME) often gives good agreement with observed data. We used empirical data from several MME studies, plus theoretical arguments, to better understand why and when the MME mean will be a good predictor. This should help modelers decide how to create and use MMEs for climate impact assessment. [ABSTRACT FROM AUTHOR]

Nitrogen fertilization is one of the major issues of crop productions in west-Europe. Every year, large quantities of synthetic nitrogen are used to fertilize crops, such as winter wheat (Triticum aestivum), and ensure them high yielding conditions. In return, substantial amount of nitrogen ends up in the environment where it generates pollutions. The combination of strategic and tactical approaches has been suggested to improve the accuracy of nitrogen fertilization decisions and their implementation. Combining strategic and tactical approaches requires a detailed understanding of the dynamic of nitrogen use by the plant to both anticipate and adapt fertilization practices. Inspired by the concept of yield component phenotypic plasticity, this study aimed to analyze the influence of nitrogen fertilization dynamics on yield elaboration and infer strategic decision rules dedicated to wheat yield optimization in a Belgian context. The analysis, implemented from ten-year experiment, was conducted in two different steps. (1) We characterized the effect of nitrogen fertilization on yield and yield components when the crop was submitted to different agro-pedo-climatic contexts, (2) we analyzed the specific influence of nitrogen fertilization dynamic on yield elaboration, normalizing the variables of the experiment to exclude genetic and environmental influences. The study revealed that highest nitrogen fertilization did not automatically lead to the highest yields. High nitrogen fertilization contributed to increase the negative trade-offs between yield components, which limited the efficiency of nitrogen supplies. Conversely, a balanced dynamic of nitrogen supplies was essential to reach high yields while supplying effective amount of nitrogen. From this Belgian dataset, optimal nitrogen fertilization strategies supposed early nitrogen supplies split in two applications to support the initiation phases of grains per m² and another nitrogen supply at flag leaf stage to maintain components of grain per m² while still allowing grain filling. This fertilization structures, adapted to different pedoclimatic contexts would support strategic approach in crop nitrogen nutrition. • The effect of N on yield components plasticity was analyzed in a ten-year experiment. • Environmental influences were excluded by the normalization of experiment variables. • Highest yields did not always result from highest nitrogen fertilization. • Balanced nitrogen fertilization ensures high yields and rational nitrogen supply. • Decision rules were infered from the analysis to support N fertilization strategical approach. [ABSTRACT FROM AUTHOR]

• The approach computes daily CO 2 fluxes (GPP, RECO, NEE) from soil-crop model outputs. • The 16-year ICOS crop rotation of BE-LON was used to set-up the approach. • Goodness of fit indicators proved the method to be reliable. • Environmental drivers of CO 2 fluxes inter-seasonal variability were identified. • Its genericity makes it a valuable tool to investigate crop rotations CO 2 dynamics. Carbon emissions in agriculture play a major role in climate change. Modelling studies enable to investigate the impacts of climate change in crops, accounting for soil organic carbon feedbacks and CO 2 concentrations. But it is primordial that crop models properly consider the CO 2 exchanges at the level of crop rotations beyond the cycle of a single crop. With this goal in mind, we used the outputs of the soil-crop model STICS in its standard pre-parameterized version to model (i) the Gross Primary Productivity (GPP), derived from the autotrophic respiration and the Net Primary Productivity, which is computed through the daily change in plant carbon (C) pools; (ii) the Ecosystem Respiration (RECO), with the autotrophic component being derived from the plant biomass, plant nitrogen concentration and GPP, and the heterotrophic component from the mineralization of residues and organic matter; and (iii) the Net Ecosystem Exchange, equal to the sum of GPP and RECO. The comparison of simulations with field observations indicates that the model is able to simulate accurately daily CO 2 fluxes originating from a long-term and diversified crop rotation (efficiency EF equal to 0.79 for GPP, 0.59 for RECO and 0.67 for NEE). Concerning the evaluation of the cumulated fluxes over the 16-year rotation, the model is able to evaluate it accurately for RECO, with a slight underestimation (normalized deviation ND = 15.7%), and very accurately for GPP (ND = 5.12%). But for NEE, the relative overestimation is higher (ND = 62.2%), indicating that a more precise estimation of HR is required to obtain reliable net C budgets. The model also succeeds to capture the trends in the influence of several environmental drivers on CO 2 fluxes. It globally proves to be a valuable tool in the investigation of CO 2 exchanges of crop rotations in historical and future climatic conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. [ABSTRACT FROM AUTHOR]

• Deep learning method to count and segment wheat ears in RGB images. • Validated for all the key development stages: from heading to maturity. • Stereovision provides image footprint, that allows to retrieve ear density. • The algorithm outperformed three human operators to estimate ear density. Recent deep learning methods have allowed important steps forward in the automatic detection of wheat ears in the field. Nevertheless, it was still lacking a method able to both count and segment the ears, validated at all the development stages from heading to maturity. Moreover, the critical step of converting the ear count in an image to an ear density, i.e. a number of ears per square metre in the field, has been widely ignored by most of the previous studies. For this research, wheat RGB images have been acquired from heading to maturity in two field trials displaying contrasted fertilisation scenarios. An unsupervised learning approach on the YOLOv5 model, as well as the cutting-edge DeepMAC segmentation method were exploited to develop a wheat ear counting and segmentation pipeline that necessitated only a limited amount of labelling work for the training. An additional label set including all the development stages was built for validation. The average F1 score of ear bounding box detection was 0.93 and the average F1 score of segmentation was 0.86. To convert the ear counts to ear densities, a second RGB camera was used so that the distance between the cameras and the ears could be measured by stereovision. That distance was exploited to compute the image footprint at ear level, and thus divide the number of ears by this footprint to get the ear density. The obtained ear densities were coherent regarding the fertilisation scenarios but, for a same fertilisation, differences were observed between acquisition dates. This highlights that the measurement was not able to retrieve absolute ear densities for all the development stages and conditions. The deep learning measurement considered the most reliable outperformed observations from three human operators. [ABSTRACT FROM AUTHOR]

The length of MOSFET channels is an important circuit design parameter, and this paper focuses on a new industrially-compatible technique using gate-to-channel measurements Cgc(Vg) to provide accurate extraction of the channel length. Thanks to fully-automatic probers, the technique provides large scale extractions and so, statistical-based results can be extracted with a maximized reliability. An in-depth study of parasitic capacitances has been performed to improve the extraction accuracy to within a few nanometers. The proposed methodology has been upgraded to in-line monitoring tests and deployed in a 300-mm semiconductor manufacturing facility. The pertinence of accurate channel length extraction has finally been proved by experimental studies (hot carrier injection lifetime), validating the coherence and the reliability of this technique. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is an essential macronutrient for crop growth and food production since it is involved in many biochemical processes in plants. Its deficiency causes physiological, biochemical, and morphological changes. These visual changes occur when the photosynthetic apparatus system is already damaged, and they are often irreversible. Consequently, it is necessary to develop novel methods for determining nutritional plant status early in the season for rapid intervention in crop production. This challenge can be tackled using emerging techniques for crop monitoring and rapid detection of stresses. Chlorophyll a fluorescence has proved its potential to detect early abiotic stresses effects on photosynthetic efficiency. Here, we evaluate the potential of this technique to assess phosphorus status in wheat leaves (Triticum durum L.), grown in different concentrations of phosphorus insured by different fertilizer forms (two orthophosphates and one polyphosphate). Results have shown a significant effect of the applied P concentration and fertilizer form on P leaves uptake at an early development stage and improved chlorophyll content index. Phosphorus level and fertilizer form were found to affect also the kinetics of the Chlorophyll a fluorescent transient (OJIP-transient) and related fluorescence parameters. Three predictive models were generated and compared. The results from partial least squares regression (PLSR) performed on fluorescence parameters with cross-validation (CV) were: the root mean square error and the determination coefficient of cross-validation (RMSECV = 0.095 and R2 = 0.67, respectively). The obtained model predicted phosphorus leaf content with RMSE of prediction (RMSEP) of 0.079 g 0.100 g−1 and R2 of 0.56. Results showed that the generated PLS predictive model using chlorophyll a fluorescence parameters has some potential to detect phosphorus status in wheat fresh leaves and can be highly informative about the photosynthetic apparatus state under P deficiency. • Significant effect of the applied phosphorus concentration and fertilizer form on phosphorus leaves uptake. • Chlorophyll a fluorescence was used to evaluate the potential of this technique to assess phosphorus status in wheat leaves. • Phosphorus level and fertilizer form were found to affect also chlorophyll a fluorescent transient. • Analyzing a large number of fluorescence parameters by applying PCA can detect phosphorus status in durum wheat leaves. [ABSTRACT FROM AUTHOR]

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. • We propose a new approach to calibration recommendations for process-based models. • Approach is based on analyzing calibration in multi-model simulation exercises. • Resulting recommendations are holistic and anchored in actual practice. • We derive calibration recommendations for crop models used to simulate phenology. • Recommendations concern: objective function, parameters to estimate, software used. [ABSTRACT FROM AUTHOR]

There is growing evidence that agroecology can reconcile the environmental, economic, and social pillars of agricultural sustainability. However, teaching and learning agroecology is challenging, especially since most agricultural graduate programs in Europe are not adapted to teach the diversity of its related practices. To improve agroecology learning, we built the online simulation game SEGAE. This article illustrates the game's relevance for learning agroecology. The game is based on a modeling framework that gamifies the implementation of agroecological practices in an integrated crop-livestock farm and assesses their impacts on sustainability. To do so, SEGAE is based on an output-oriented approach that represents impacts of practices on various indicators. These impacts are included in a matrix, which is associated with a dynamic graphical interface accessible to players. Two examples of game sessions were developed to illustrate the game's potential. In the first example, players can gain knowledge about agroecological practices by implementing practices that improve soil quality and assessing their impacts on sustainability. Results of this example place the farm's improved overall sustainability into perspective with its reduced food production potential. In the second example, players can improve their skills in transition management and acquire a systems approach by converting the farm to organic farming within five years. Results of this example prompt discussion of the steps needed to obtain organic certification and the coherence between crop and animal production needed to foster sustainability. SEGAE was designed to strengthen European training in agroecology, and active contributions from users would help to improve this tool, extend it to new farming systems and forge connections within the community of teachers working on agroecology. [Display omitted] • Agroecology can improve agricultural sustainability but teaching and learning agroecology is challenging • This article presents an online simulation game built to improve agroecology learning, SEGAE, and illustrates its potential • SEGAE can prompt discussion on steps and trade-offs when increasing sustainability in an integrated crop-livestock farm • SEGAE helps to acquire a systems approach and improve skills in agroecological transition management • SEGAE was designed to strengthen training in agroecology, and active contributions from users can help to improve this tool [ABSTRACT FROM AUTHOR]

• A large multi-model study predicting wheat phenology in Australia was performed. • Calibration and evaluation datasets were independently drawn from the same population. • Mean absolute prediction error ranged from 6 to 20 days (median 9 days). • Two thirds of modeling groups predicted better than a simple temperature sum. • Variability between groups using the same model structure was substantial. Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques. [ABSTRACT FROM AUTHOR]

• 27 modeling groups were evaluated for wheat phenology predictions for France. • Calibration and evaluation data were sampled from the same target population. • The calibration and evaluation data have neither year nor site in common. • The best groups had a mean absolute error comparable to the measurement error. • Model structure alone does not determine prediction accuracy. Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population. [ABSTRACT FROM AUTHOR]

In recent decades, remote sensing has increasingly been used to estimate the spatio-temporal evolution of crop biophysical parameters such as the above-ground biomass (AGB). On a local scale, the advent of unmanned aerial vehicles (UAVs) seems to be a promising trade-off between satellite/airborne and terrestrial remote sensing. This study aims to evaluate the potential of a low-cost UAV RGB solution to predict the final AGB of Zea mays. Besides evaluating the interest of 3D data and multitemporality, our study aims to answer operational questions such as when one should plan a combination of two UAV flights for AGB modeling. In this case, study, final AGB prediction model performance reached 0.55 (R-square) using only UAV information and 0.8 (R-square) when combining UAV information from a single flight with a single-field AGB measurement. The adding of UAV height information to the model improves the quality of the AGB prediction. Performing two flights provides almost systematically an improvement in AGB prediction ability in comparison to most single flights. Our study provides clear insight about how we can counter the low spectral resolution of consumer-grade RGB cameras using height information and multitemporality. Our results highlight the importance of the height information which can be derived from UAV data on one hand, and on the other hand, the lower relative importance of RGB spectral information. [ABSTRACT FROM AUTHOR]